TY - GEN

T1 - Fast adaptive Bayesian beamforming using the FFT

AU - Lam, C. J.

AU - Singer, A. C.

N1 - Publisher Copyright:
© 2003 IEEE.
Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.

PY - 2003

Y1 - 2003

N2 - A fast algorithm is developed to implement a Bayesian beam-former that can estimate signals of unknown direction of arrival (DOA). In the Bayesian approach, the underlying DOA is assumed random and its a posteriori probability density function (PDF) is approximated by a discrete probability mass function. A Bayesian beamformer then balances a set of beamformers according to the associated weights. To obtain a close approximation of the a posteriori PDF, the number of samples must be sufficiently large, incurring a significant computational burden. In this paper, we exploit the structure of a uniform linear array (ULA) to show that samples of the a posteriori PDF can be computed efficiently using the fast Fourier transform (FFT). This leads to a fast algorithm for the Bayesian beamformer, which operates in O(MlogM + N2) operations where M is the number of samples and N is the number of sensors.

AB - A fast algorithm is developed to implement a Bayesian beam-former that can estimate signals of unknown direction of arrival (DOA). In the Bayesian approach, the underlying DOA is assumed random and its a posteriori probability density function (PDF) is approximated by a discrete probability mass function. A Bayesian beamformer then balances a set of beamformers according to the associated weights. To obtain a close approximation of the a posteriori PDF, the number of samples must be sufficiently large, incurring a significant computational burden. In this paper, we exploit the structure of a uniform linear array (ULA) to show that samples of the a posteriori PDF can be computed efficiently using the fast Fourier transform (FFT). This leads to a fast algorithm for the Bayesian beamformer, which operates in O(MlogM + N2) operations where M is the number of samples and N is the number of sensors.

KW - Array signal processing

KW - Bayesian methods

KW - Computational efficiency

KW - Direction of arrival estimation

KW - Fast Fourier transforms

KW - Frequency estimation

KW - Probability density function

KW - Radar

KW - Sonar

KW - Vectors

UR - http://www.scopus.com/inward/record.url?scp=34547775302&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34547775302&partnerID=8YFLogxK

U2 - 10.1109/SSP.2003.1289434

DO - 10.1109/SSP.2003.1289434

M3 - Conference contribution

AN - SCOPUS:34547775302

T3 - IEEE Workshop on Statistical Signal Processing Proceedings

SP - 413

EP - 416

BT - Proceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003

PB - IEEE Computer Society

T2 - IEEE Workshop on Statistical Signal Processing, SSP 2003

Y2 - 28 September 2003 through 1 October 2003

ER -